A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies

Area of Science:

• Genomics
• Statistical Genetics
• Human Genetics

Background:

• Large-scale whole-genome sequencing (WGS) studies have advanced understanding of rare variant contributions to complex traits.
• Multi-trait analysis can increase statistical power and detect pleiotropic genes compared to single-trait approaches.
• Current multi-trait methods are inadequate for large-scale WGS rare variant analysis.

Purpose of the Study:

• To introduce MultiSTAAR, a scalable statistical framework and pipeline for functionally informed multi-trait rare variant analysis in large-scale WGS studies.
• To improve the power of rare variant association analysis by jointly considering multiple traits and functional annotations.

Main Methods:

• MultiSTAAR jointly analyzes multiple traits, accounting for relatedness, population structure, and phenotype correlations.
• The framework incorporates multiple functional annotations to enhance rare variant association analysis.
• Applied to three lipid traits in 61,838 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program.

Main Results:

• MultiSTAAR successfully identified novel associations with lipid traits.
• These discoveries were missed by traditional single-trait analysis methods.
• The study demonstrated the effectiveness of MultiSTAAR in large-scale WGS data.

Conclusions:

• MultiSTAAR provides a powerful and scalable solution for multi-trait rare variant analysis in large WGS studies.
• The method enhances the detection of genetic associations for complex human traits.
• This framework has significant implications for precision medicine and understanding genetic contributions to disease.